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• W2018184023 abstract "An important reason for analyzing panel data is to observe the dynamic nature of an economic variable separately from its time-invariant unobserved heterogeneity. This paper examines how to estimate the autocovariances of a variable separately from its time-invariant unobserved heterogeneity. When both cross-sectional and time series sample sizes tend to infinity, we show that the within-group autocovariances are consistent, although they are severely biased when the time series length is short. The biases have the leading term that converges to the long-run variance of the individual dynamics. This paper develops methods to estimate the long-run variance in panel data settings and to alleviate the biases of the within-group autocovariances based on the proposed long-run variance estimators. Monte Carlo simulations reveal that the procedures developed in this paper effectively reduce the biases of the estimators for small samples." @default.
• W2018184023 created "2016-06-24" @default.
• W2018184023 creator A5054811135 @default.
• W2018184023 date "2010-02-17" @default.
• W2018184023 modified "2023-10-06" @default.
• W2018184023 title "ASYMPTOTICALLY UNBIASED ESTIMATION OF AUTOCOVARIANCES AND AUTOCORRELATIONS WITH LONG PANEL DATA" @default.
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• W2018184023 doi "https://doi.org/10.1017/s0266466609990582" @default.
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